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diff --git a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInDisk.R b/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInDisk.R
deleted file mode 100644
index dd43cf7b..00000000
--- a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInDisk.R	
+++ /dev/null
@@ -1,284 +0,0 @@
-#############################################################
-#CRM Demo on CDNOW
-#Microsoft 
-#############################################################
-##check if necessary R packages are installed
-install.packages("RODBC")
-
-library(RODBC)
-
-##set directory
-setwd("C:/Users/zhouf/Documents/Revolution Analytics/Demo/RREDemo/CRM")
-data.path<-"C:/Users/zhouf/Documents/Revolution Analytics/Demo/RREDemo/CRM/Data/CDNOW_master"
-output.path<-"C:/Users/zhouf/Documents/Revolution Analytics/Demo/RREDemo/CRM/Output/Xdf"
-
-##Set Compute Context
-#rxOptions(computeContext=RxLocalSeq())
-rxOptions(computeContext=RxLocalParallel())
-rxOptions(xdfCompressionLevel = -1)
-rxOptions(reportProgress=1)
-#-----------------------------------------------------------------------------------------------------------------------------------------------
-
-##Connect to SQL database via RxOdbcData
-CRMSQL = "SELECT * FROM dbo.CDNOW"
-CRMDS<- RxOdbcData(sqlQuery = CRMSQL,
-	       connectionString = "Driver={SQL Server Native Client 11.0};
-	                           Server=tcp:sqlserver2012-81yms1ai.cloudapp.net,57500;
-							   Database=RREDemoSql;Uid=zhouf;Pwd=Microsoft1;")
-						
-CRMDS<- RxOdbcData(sqlQuery = CRMSQL,
-	       connectionString = "Driver=SQL Server;
-		                      Server=tcp:192.168.220.128,1433;
-							  Database=sqlr;
-							  Uid=sa;
-							  Pwd=Microsoft1")						
-
-#CRMDS<- RxOdbcData(sqlQuery = CRMSQL,
-#	connectionString = "Driver={SQL Server Native Client 11.0};Server=tcp:168.63.172.116,57500;Database=RREDemoSql;Uid=zhouf;Pwd=Microsoft1;")
-#CRMDS<- RxOdbcData(sqlQuery = CRMSQL,
-#connectionString = "DSN=MiningDatabaseSql;Uid=zhouf;Pwd=Microsoft1;")
-#CRMDataFile <- RxXdfData("CDNOW_master.xdf")
-CRMDataFile<-file.path(output.path,"CDNOW_master.xdf")
-##Read data with RRE
-dfcolInfo<-list(
-	ID=list(type="factor"),
-	Date=list(type="character"),
-	Volume=list(type="numeric"),
-	Amount=list(type="numeric")
-	)
-rxImport(inData=CRMDS,outFile =CRMDataFile,colInfo=dfcolInfo,overwrite =TRUE,useFastRead=TRUE)
-#rxImport(inData=CRMDS,outFile =CRMDataFile,overwrite =TRUE,useFastRead=TRUE)
-rxGetInfo(CRMDataFile,getVarInfo=T,numRows=3)
-#------------------------------------------------------------------------------------------------------------------------------------------------------------
-
-##Connect to SQL database via odbcConnect
-#channel<-odbcConnect("Driver={SQL Server Native Client 11.0};Server=tcp:sqlserver2012-81yms1ai.cloudapp.net,57500;Database=AIAPoVSql;Uid=zhouf;Pwd=Microsoft2;")
-channel<-odbcConnect(dsn="MiningDatabaseSql",uid="zhouf",pwd="Microsoft1")
-
-channel<-odbcDriverConnect(connection="Driver=SQL Server;
-		                      Server=tcp:192.168.176.132,1433;
-							  Database=sqlr;
-							  Uid=sa;
-							  Pwd=Microsoft1")
-##Read data from SQL via OSR
-aia<-sqlQuery(channel,paste(" select * from dbo.AIOE_MODAL_RAWDATAL_revised"))
-df<-sqlFetch(channel, 'CDNOW')
-df<- sqlQuery(channel,paste("select * from dbo.CDNOW"))
-#df.csv <- file.path(data.path,"CDNOW_master.csv")
-df.xdf<-file.path(output.path,"CDNOW_master.xdf")
-dfcolInfo<-list(
-	ID=list(type="factor"),
-	Date=list(type="Date"),
-	Volume=list(type="numeric"),
-	Amount=list(type="numeric")
-	)
-rxImport(inData=df,outFile =df.xdf,colInfo=dfcolInfo,overwrite =TRUE,useFastRead=F)
-rxGetInfo(df.xdf,getVarInfo=T,numRows=3)
-#------------------------------------------------------------------------------------------------------
-
-df.xdf<-file.path(output.path,"CDNOW_master.xdf")
-df1.xdf<-file.path(output.path,"CDNOW_master1.xdf")
-rxDataStep(inData=df.xdf,outFile=df1.xdf,
-	    transforms=list(
-		Date=as.Date(Date,"%Y-%m-%d")
-		),
-	    removeMissings=TRUE,
-	    overwrite=T)
-rxGetInfo(df1.xdf,getVarInfo=T,numRows=3)
-#rxSummary(~ID+Date+Volume+Amount,df.xdf)
-
-##Step 1: RFM Model
-# set the startDate and endDate, we will only analysis the records in this date range
-RFMSQL="SELECT * FROM dbo.RFM_Result"
-RFMDS<- RxOdbcData(sqlQuery = RFMSQL,
-	       connectionString = "Driver=SQL Server;
-		                      Server=tcp:192.168.176.130,1433;
-							  Database=sqlr;
-							  Uid=sa;
-							  Pwd=Microsoft1")			
-RFMResult.xdf<-file.path(output.path,"RFM_Result.xdf")
-rxImport(inData=RFMDS,outFile =RFMResult.xdf,overwrite =TRUE,useFastRead=TRUE)
-rxGetInfo(RFMResult.xdf,getVarInfo=T,numRows=3)
-#visualize the RFM values
-rxHistogram(~R,data=RFMResult.xdf, xNumTicks=20)
-rxHistogram(~F,data=RFMResult.xdf,rowSelection=F<30,
-            xNumTicks=20)
-rxHistogram(~M,data=RFMResult.xdf,rowSelection=M<200,
-            xNumTicks=20)  
-
-#count frequency of each RFMscore Level
-tmp<-rxCube(~F(Total_Score),data=RFMResult.xdf)
-results <- rxResultsDF(tmp)
-results<-results[results$Counts!=0,]
-results[order(results$Counts,decreasing=TRUE),]
-
-##Step 2: K-means Clustering
-
-#K-means Clustering 
-Kmeans.xdf<-file.path(output.path,"Kmeans.xdf")
-md.km <- rxKmeans(formula=~R+F+M+R_Score+F_Score+M_Score, 
-						data = RFMResult.xdf, 
- 						outFile =Kmeans.xdf,
-						numClusters=8,
-						algorithm = "lloyd",
-						writeModelVars=TRUE,
-						overwrite=TRUE)
-rxGetInfo(Kmeans.xdf,getVarInfo=TRUE,numRows=10)
-
-centers<-round(md.km$centers,digits=2)
-size<-md.km$size
-centers.txt<-file.path(output.path,"centers.txt")
-write.table(centers,file=centers.txt,sep=" ")
-size.txt<-file.path(output.path,"size.txt")
-write.table(size,file=size.txt,sep=" ")
-
-mdDf <- rxXdfToDataFrame(file=Kmeans.xdf)
-head(mdDf)
-plot(mdDf[,2:7],col=mdDf$.rxCluster)
-title(main="RFM-based K-means on CDNOW Data",line=3)	
-##Step 3: Prediction-logistic and decision tree
-
-##1.Prediction on Personal Customer Data without considering age and sex
-#Create IsVIP variable
-RFMVIP.xdf<-file.path(output.path,"RFMVIP.xdf")
-rxDataStep(inData=RFMResult.xdf,outFile=RFMVIP.xdf,
-         transforms=list(IsVIP=ifelse(Toltal_Score>=441,1,0)),
-        overwrite=TRUE,reportProgress=1
-)
-RFMVIPInfo<-rxGetInfo(RFMVIP.xdf,getVarInfo=T,numRows=3)
-
-#Write IsVIP and Cluster result to SQL database 
-RFMVIP.df<-rxXdfToDataFrame(file=RFMVIP.xdf)
-Cluster<-factor(mdDf$.rxCluster)
-RFMVIPCluster<-cbind(RFMVIP.df,Cluster)
-sqlSave(channel,RFMVIPCluster,rownames=FALSE,append=FALSE,varTypes=list(numeric="float",integer="int",Date="date"))
-
-
-#Creat training/testing data set
-RD<-sample(1:10,RFMVIPInfo$numRows,replace=TRUE)
-str(RD)
-table(RD)
-RFMVIPCluster$RD<-RD;
-RFMVIPCluster.xdf<-file.path(output.path,"RFMVIPCluster.xdf")
-rxDataFrameToXdf(data=RFMVIPCluster,outFile=RFMVIPCluster.xdf,overwrite=TRUE)
-
-TrainTest.xdf<-file.path(output.path,"TrainTest.xdf")
-rxDataStep(inData=RFMVIPCluster.xdf,outFile=TrainTest.xdf,
-	       transforms=list(
-			urv=factor(ifelse(RD<=8,'TRAIN','TEST'))
-			),
-           overwrite=T)
-rxGetInfo(TrainTest.xdf,T,numRows=3)
-
-##Split data into training/testing data set
-train.xdf <- file.path(output.path,'TrainTest.urv.TRAIN.xdf')
-test.xdf <- file.path(output.path,'TrainTest.urv.TEST.xdf')
-
-rxSplit(TrainTest.xdf,outFilesBase=TrainTest.xdf,splitByFactor='urv',overwrite=T,reportProgress=1)
-
-##Build our Logistic Regression Model with IsVIP as response
-r1<- rxLogit(IsVIP~R+F+M,
-data =train.xdf,
-variableSelection = rxStepControl(method="stepwise",
-scope = ~ R+F+M))
-summary(r1)
-
-
-##r1:stepwise selection shows that Monetary,Recency are significant.
-##Build our Logistic REgression MOdel
-r2<- rxLogit(IsVIP ~R+F,
-data =train.xdf,covCoef=TRUE)
-summary(r2)
-
-##Predict our Logistic Model on our test Dataset
-LogisticPred.xdf<-file.path(output.path,"LogisticPred.xdf")
-rxPredict(r2,data=test.xdf,outData=LogisticPred.xdf,writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=TRUE,computeStdErr = TRUE,
-interval = "confidence",predVarNames='LogitPredict',overwrite=TRUE)
-rxGetInfo(LogisticPred.xdf,getVarInfo=T,numRows=10)
-LogisticPredInfoTop10<-rxGetInfo(LogisticPred.xdf,getVarInfo=T,numRows=10)
-LogisticPredInfoTop10.txt<-file.path(output.path,"LogisticPredInfoTop10.txt")
-write.table(LogisticPredInfoTop10$data,file=LogisticPredInfoTop10.txt,sep=" ")
-
-LogisticPred<-rxXdfToDataFrame(file=LogisticPred.xdf)
-sqlSave(channel,LogisticPred,rownames=FALSE,append=FALSE,varTypes=list(numeric="float",integer="int",Date="date"))
-
-
-##Draw a ROC curve
-rxRocCurve(actualVarName='IsVIP',predVarNames='LogitPredict',data=LogisticPred.xdf)
-
-##Build a Decision Tree with Cluster as response
-d1 <- rxDTree(Cluster~R+F+M,data=train.xdf,blocksPerRead=5)
-d1 <- rxDTree(Cluster~R_Score+F_Score+M_Score,data=train.xdf,blocksPerRead=5)
-d1Cp<- rxDTreeBestCp(d1)
-d1 <- prune.rxDTree(d1, cp=d1Cp)
-d1
-d2 <- rxDTree(Cluster~R+F+M,data=train.xdf, pruneCp="auto")
-d2 <- rxDTree(Cluster~R_Score+F_Score+M_Score,data=train.xdf, pruneCp="auto")
-d2
-
-#View Decision Tree
-#View 1
-library(RevoTreeView)
-plot(createTreeView(d1))
-plot(createTreeView(d2))
-
-#View 2
-library(rpart)
-plot(rxAddInheritance(d1))
-text(rxAddInheritance(d1))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
-plot(rxAddInheritance(d2))
-text(rxAddInheritance(d2))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
-
-#Prediction
-DTreePred.xdf<-file.path(output.path,"DTreePred.xdf")
-rxPredict(d2, data=test.xdf,outData=DTreePred.xdf,predVarNames=c('prob1','prob2','prob3','prob4','prob5','prob6','prob7','prob8'),writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=T,overwrite=TRUE)
-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=3)
-DTreePredInfoTop10<-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=10)
-DTreePredInfoTop10.txt<-file.path(output.path,"DTreePredInfoTop10.txt")
-write.table(DTreePredInfoTop10$data,file=DTreePredInfoTop10.txt,sep=" ")
-
-DTreePred<-rxXdfToDataFrame(file=DTreePred.xdf)
-sqlSave(channel,DTreePred,rownames=FALSE,append=FALSE,varTypes=list(numeric="float",integer="int",Date="date"))
-odbcClose(channel)
-
-#-------------------------------------------------------------------------------------
-##Build a Decision Tree with IsVIP as response
-d<-rxDTree(IsVIP~R+F+M,data=train.xdf,maxDepth=5,blocksPerRead=5)
-d1 <- rxDTree(IsVIP~R+F+M,data=train.xdf,blocksPerRead=5)
-d1
-d1Cp<- rxDTreeBestCp(d1)
-d1 <- prune.rxDTree(d1, cp=d1Cp)
-d2 <- rxDTree(IsVIP~R+F+M,data=train.xdf, pruneCp="auto")
-d2
-
-#View Decision Tree
-#View 1
-library(RevoTreeView)
-plot(createTreeView(d))
-plot(createTreeView(d1))
-plot(createTreeView(d2))
-
-#View 2
-library(rpart)
-plot(rxAddInheritance(d))
-text(rxAddInheritance(d))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
-plot(rxAddInheritance(d1))
-text(rxAddInheritance(d1))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
-plot(rxAddInheritance(d2))
-text(rxAddInheritance(d2))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
-
-#Prediction
-DTreePred.xdf<-file.path(output.path,"DTreePred.xdf")
-rxPredict(d, data=test.xdf,outData=DTreePred.xdf,writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=T,overwrite=TRUE)
-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=3)
-DTreePredInfoTop10<-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=10)
-DTreePredInfoTop10.txt<-file.path(output.path,"DTreePredInfoTop10.txt")
-write.table(DTreePredInfoTop10$data,file=DTreePredInfoTop10.txt,sep=" ")
-
-#Draw ROC
-rxRocCurve(actualVarName='IsVIP',predVarNames='IsVIP_Pred',data=DTreePred.xdf)
\ No newline at end of file
diff --git a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInMemory.R b/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInMemory.R
index f81c5d87..3f622372 100644
--- a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInMemory.R	
+++ b/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInMemory.R	
@@ -1,173 +1,237 @@
 #############################################################
-#CRM Demo on CDNOW
-#Microsoft
+# Title : CRM Demo in-memory
+# Author: Microsoft
+# Date: Dec, 2015
 #############################################################
 
-#install package
+# Install package
+
 install.packages("rmarkdown")
 install.packages("fpc")
 
-#set directory
-setwd("C:/Users/zhouf/Documents/Revolution Analytics/Demo/RREDemo/CRM")
-data.path<-"C:/Users/zhouf/Documents/Revolution Analytics/Demo/RREDemo/CRM/Data/CDNOW_master"
+# Set directory
 
+wd <- getwd()
+data.path <- file.path(wd, "Data", "CDNOW_master.csv")
+
+# Connect to SQL database using ODBC and read data from SQL via Open Source R
 
-##connect to SQL database using ODBC and read data from SQL via Open Source R
 library(RODBC)
 getSqlTypeInfo()
-#connect from local PC
-channel<-odbcDriverConnect("driver={SQL Server Native Client 11.0};server=tcp:sqlserver2012-81yms1ai.cloudapp.net,57500;database=RREDemoSql;uid=zhouf;pwd=Microsoft2;")
-#connect from Windows201201
-channel<-odbcConnect(dsn="MiningDatabaseSql",uid="zhouf",pwd="Microsoft1")
-#connect with error
-channel<-odbcDriverConnect('driver={SQL Server};server=tcp:192.168.220.128,1433;database=sqlr;uid=sa;pwd=Microsoft1')
-						
 
-df<-sqlFetch(channel, 'CDNOW')
-df<- sqlQuery(channel,paste("select * from dbo.CDNOW"))
+# Connect from local PC
+
+channel <- odbcDriverConnect("driver={SQL Server Native Client 11.0};
+                              server=tcp:sqlserver2012-81yms1ai.cloudapp.net,57500;
+                              database=RREDemoSql;
+                              uid=******;
+                              pwd=******;")
+
+df <- sqlFetch(channel, 'CDNOW')
+df <- sqlQuery(channel, paste("select * from dbo.CDNOW"))
+
 df$Date<-as.Date(df$Date)
+
 str(df)
 head(df)
 
-#remove the rows with the duplicated IDs to see how many customers in total
-uid <- df[!duplicated(df[,"ID"]),]
+# Remove the rows with the duplicated IDs to see how many customers in total
+
+uid <- df[!duplicated(df[,"ID"]), ]
+
 dim(uid)
 
-#call RFM source code 
-source("RFM_Analysis_R_Source_Codes_V1.3.R")
+# Step 1: RFM analysis
+
+# Call RFM source code 
+
+source(wd, "R", "RFM_Analysis_R_Source_Codes_V1.3.R")
+
+# Set the startDate and endDate, we will only analysis the records in this date range
 
-#set the startDate and endDate, we will only analysis the records in this date range
 startDate <- as.Date("19970101","%Y%m%d")
 endDate <- as.Date("19980701","%Y%m%d")
 
-#calculate RFM value
-df <- getDataFrame(df,startDate,endDate,tIDColName="ID",tDateColName="Date",tAmountColName="Amount")
+# Calculate RFM value
+
+df <- getDataFrame(df, startDate, endDate, tIDColName="ID", tDateColName="Date", tAmountColName="Amount")
+
 head(df)
 
-#obtain Independent RFM score
+# Obtain independent RFM score
+
 df1 <-getIndependentScore(df)
+
 head(df1)
 
-#draw the histograms in the R, F, and M dimensions 
+# Draw the histograms in the R, F, and M dimensions 
+
 drawHistograms(df1)
 
-S500<-df1[df1$Total_Score>500,]
+S500 <- df1[df1$Total_Score > 500, ]
 dim(S500)
 
-S400<-df1[df1$Total_Score>400,]
+S400 <- df1[df1$Total_Score > 400, ]
 dim(S400)
 
+# Obtain RFM score with breaks
+# Take a look at the distribution of R, F, M
 
-#obtain RFM score with breaks
-#take a look at the distribution of R, F, M
 par(mfrow = c(1,3))
 hist(df$Recency)
 hist(df$Frequency)
 hist(df$Monetary)
 
-#set the Recency ranges as 0-120 days, 120-240 days, 240-450 days, 450-500days, and more than 500days.
-r <-c(120,240,450,500)
-#set the Frequency ranges as 0 ? 2times, 2-5 times,5-8 times, 8-10 times, and more than 10 times.
-f <-c(2,5,8,10)
-#set the Monetary ranges as 0-10 dollars, 10-20 dollars, and so on.
+# Set the Recency ranges as 0-120 days, 120-240 days, 240-450 days, 450-500 days, and more than 500 days.
+
+r <- c(120, 240, 450, 500)
+
+# Set the Frequency ranges as 0-2 times, 2-5 times, 5-8 times, 8-10 times, and more than 10 times.
+
+f <- c(2, 5, 8, 10)
+
+# Set the Monetary ranges as 0-10 dollars, 10-20 dollars, and so on.
+
 m <-c(10,20,30,100)
 
-#calculate RFM score with breaks
-df2<-getScoreWithBreaks(df,r,f,m)
+# Calculate RFM score with breaks
+
+df2 <- getScoreWithBreaks(df, r, f, m)
 drawHistograms(df2)
 
-S500<-df2[df2$Total_Score>500,]
+S500 <- df2[df2$Total_Score > 500, ]
 dim(S500)
 
-S400<-df2[df2$Total_Score>400,]
+S400 <- df2[df2$Total_Score > 400, ]
 dim(S400)
 
-target <- df2[df2$Total_Score>=441,]
+target <- df2[df2$Total_Score >= 441,]
 dim(target)
 
-#obtain RFM scores with quantiles as breaks
+# Obtain RFM scores with quantiles as breaks
+
 r <-c(cutpoint(df$Recency))
 f <-c(cutpoint(df$Frequency))
 m <-c(cutpoint(df$Monetary))
 
-df3<-getScoreWithBreaks(df,r,f,m)
+df3 <- getScoreWithBreaks(df, r, f, m)
+
 str(df3)
 head(df3)
 tail(df3)
 
-RFM_Result_osr<-subset(df3,select=c("ID","Recency","Frequency","Monetary","R_Score","F_Score","M_Score","Total_Score"))
-colnames(RFM_Result_osr)<-c("ID","R","F","M","R_Score","F_Score","M_Score","Total_Score")
-head(RFM_Result_osr)
+RFM_Result <- subset(df3,
+                     select=c("ID", "Recency", "Frequency", "Monetary",
+                              "R_Score", "F_Score", "M_Score", "Total_Score"))
 
-system.time(
-sqlSave(channel,RFM_Result_osr,rownames=FALSE,append=FALSE,varTypes=list(numeric="float",integer="int"))
-)
+colnames(RFM_Result) <- c("ID", "R", "F", "M", "R_Score", "F_Score", "M_Score", "Total_Score")
 
-sqlUpdate(channel,df)
+head(RFM_Result)
+
+time <- system.time({
+  
+sqlSave(channel,
+        RFM_Result,
+        rownames=FALSE,
+        append=FALSE,
+        varTypes=list(numeric="float",
+                      integer="int"))
+})
+
+sqlUpdate(channel, df)
 odbcClose(channel)
 
-#Clustering using RFM
+# Clustering using RFM
+
 library(fpc)
 library(cluster)
-#kmeans clustering with cluster =8
-cl.fit1<-kmeans(RFM_Result_osr[,2:8],centers=8,iter.max=10,nstart=1)
-cl.fit2<-kmeans(RFM_Result_osr[,2:8],centers=8,iter.max=20,nstart=200)
+
+# Kmeans clustering with number of cluster equal to 8
+
+cl.fit1 <- kmeans(RFM_Result[, 2:8],
+                  centers=8,
+                  iter.max=10,
+                  nstart=1)
+
+cl.fit2 <- kmeans(RFM_Result[, 2:8],
+                  centers=8,
+                  iter.max=20,
+                  nstart=200)
 
 summary(cl.fit1)
+
 cluster<-cl.fit1$cluster
 centers<-cl.fit1$centers
 size<-cl.fit1$size
-plot(RFM_Result_osr[,2:4],col=cl.fit1$cluster)
+
+plot(RFM_Result[, 2:4], col=cl.fit1$cluster)
 title(main="K-means",line=3)	
 
-##Classification using RFM
-#Create IsVIP variable
-IsVIP<-ifelse(RFM_Result_osr[,'Total_Score']>=441,1,0);
-Cluster<-cl.fit1$cluster
-RFMVIPCluster<-cbind(RFM_Result_osr,IsVIP,Cluster)
+# Classification using RFM
+
+# Create IsVIP variable
+
+IsVIP <- ifelse(RFM_Result[,'Total_Score'] >= 441, 1, 0)
+Cluster <- cl.fit1$cluster
+RFMVIPCluster <- cbind(RFM_Result, IsVIP, Cluster)
+
+# Create training/testing data set
+
+RD <- sample(1:10, dim(RFMVIPCluster)[1], replace=TRUE)
 
-#Create training/testing data set
-RD<-sample(1:10,dim(RFMVIPCluster)[1],replace=TRUE)
 str(RD)
 table(RD)
-RFMVIPCluster$RD<-RD;
-urv=factor(ifelse(RD<=8,'TRAIN','TEST'))
-TrainTest<-cbind(RFMVIPCluster,urv)
-Train<-TrainTest[which(TrainTest$urv=="TRAIN"),]
-Test<-TrainTest[which(TrainTest$urv=="TEST"),]
 
-#logistic model
-##Build our Logistic Regression Model with IsVIP as response
-r1<-glm(IsVIP~R+F+M, data=Train, family = binomial)
+RFMVIPCluster$RD <- RD;
+
+urv <- factor(ifelse(RD <= 8,'TRAIN','TEST'))
+TrainTest <- cbind(RFMVIPCluster, urv)
+Train <- TrainTest[which(TrainTest$urv == "TRAIN"), ]
+Test <- TrainTest[which(TrainTest$urv == "TEST"), ]
+
+# Logistic model
+# Build our Logistic Regression Model with IsVIP as response
+
+r1 <- glm(IsVIP~R+F+M, data=Train, family = binomial)
 summary(r1)
 
-p1<-predict.glm(r1,data=Test,type="response")
+p1 <- predict.glm(r1, data=Test, type="response")
 head(p1)
 tail(p1)
 
-#decision tree
-#grow tree 
+# Decision tree
+# Grow tree 
+
 fit <- rpart(Cluster~R+F+M,
-  	method="class", data=Train)
+           	 method="class", 
+             data=Train)
+
+# Display the results 
+
+printcp(fit)
+
+# Visualize cross-validation results 
 
-#display the results 
-printcp(fit) 
-#visualize cross-validation results 
 plotcp(fit) 
-#detailed summary of splits
+
+# Detailed summary of splits
+
 summary(fit) 
 
-# plot tree 
+# Plot tree 
+
 library(rpart)
-plot(fit, uniform=TRUE, 
-  	main="Classification Tree for CDNOW")
+
+plot(fit, uniform=TRUE, main="Classification Tree for CDNOW")
 text(fit, use.n=TRUE, all=TRUE, cex=.8)
 
-# prune the tree 
-pfit<- prune(fit, cp= fit$cptable[which.min(fit$cptable[,"xerror"]),"CP"])
+# Prune the tree 
+
+pfit <- prune(fit, cp=fit$cptable[which.min(fit$cptable[,"xerror"]), "CP"])
+
+# Plot the pruned tree 
 
-# plot the pruned tree 
 plot(pfit, uniform=TRUE, 
-  	main="Pruned Classification Tree for CDNOW")
+  	 main="Pruned Classification Tree for CDNOW")
 text(pfit, use.n=TRUE, all=TRUE, cex=.8)
 
diff --git a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInSql.R b/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInSql.R
index b829bed5..66ce7c87 100644
--- a/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInSql.R	
+++ b/samples/features/r-services/Retail Precision Marketing/R/CRMonCDNOWInSql.R	
@@ -1,157 +1,203 @@
 ################################################################
-##CRM Demo in-SQL compute context
-##Microsoft
+# Title: CRM Demo in-SQL 
+# Author: Microsoft
+# Date: Dec, 2015
 #################################################################
 
-connectionString <- "Driver=SQL Server;Server=tcp:192.168.176.130,1433;Database=sqlr;Uid=sa;Pwd=Microsoft1"
+# Specify connection string and compute context
 
-RFMData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFM_Result")
+connectionString <- "Driver=SQL Server;
+                     Server=tcp:192.168.176.130,1433;
+                     Database=sqlr;
+                     Uid=******;
+                     Pwd=******"
+
+RFMData <- RxSqlServerData(connectionString=connectionString,
+                           table="RFM_Result")
         
-cc <- RxInSqlServer(connectionString = connectionString, autoCleanup = FALSE, consoleOutput = TRUE)
+cc <- RxInSqlServer(connectionString=connectionString, 
+                    autoCleanup=FALSE, 
+                    consoleOutput=TRUE)
+
 rxSetComputeContext(cc)
 
-rxGetInfo(RFMData,getVarInfo=T,numRows=3)
+rxGetInfo(RFMData, getVarInfo=T, numRows=3)
 
-#Step 1: RFM analysis
-#visualize the RFM values
-rxHistogram(~R,data=RFMData, xNumTicks=20)
-rxHistogram(~F,data=RFMData,rowSelection=F<30,
-            xNumTicks=20)
-rxHistogram(~M,data=RFMData,rowSelection=M<200,
-            xNumTicks=20)  
+# Step 1: RFM analysis
 
-#count frequency of each RFMscore Level
-tmp<-rxCube(~F(Toltal_Score),data=RFMData)
+# Visualize the RFM values
+
+rxHistogram(~R, data=RFMData, xNumTicks=20)
+rxHistogram(~F, data=RFMData, rowSelection=F < 30, xNumTicks=20)
+rxHistogram(~M, data=RFMData, rowSelection=M < 200, xNumTicks=20)  
+
+# Count frequency of each RFMscore Level
+
+tmp <- rxCube(~F(Toltal_Score), data=RFMData)
 results <- rxResultsDF(tmp)
-results<-results[results$Counts!=0,]
-results[order(results$Counts,decreasing=TRUE),]
+results <- results[results$Counts != 0, ]
+results[order(results$Counts, decreasing=TRUE), ]
+
+# Step 2: K-means Clustering 
+
+KmeansData <- RxSqlServerData(connectionString=connectionString,
+                              table = "Kmeans_Result")
 
-#Step 2: K-means Clustering 
-KmeansData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "Kmeans_Result")
 md.km <- rxKmeans(formula=~R+F+M+R_Score+F_Score+M_Score, 
-						data = RFMData, 
- 						outFile =KmeansData,
-						numClusters=8,
-						algorithm = "lloyd",
-						writeModelVars=TRUE,
-						overwrite=TRUE)
-rxGetInfo(KmeansData,getVarInfo=TRUE,numRows=10)
+						      data=RFMData, 
+ 						      outFile=KmeansData,
+						      numClusters=8,
+						      algorithm="lloyd",
+						      writeModelVars=TRUE,
+						      overwrite=TRUE)
 
-centers<-round(md.km$centers,digits=2)
-size<-md.km$size
-centers.txt<-file.path(output.path,"centers.txt")
-write.table(centers,file=centers.txt,sep=" ")
-size.txt<-file.path(output.path,"size.txt")
-write.table(size,file=size.txt,sep=" ")
+rxGetInfo(KmeansData, getVarInfo=TRUE, numRows=10)
+
+centers <- round(md.km$centers, digits=2)
+size <- md.km$size
+centers.txt <- file.path(output.path, "centers.txt")
+write.table(centers, file=centers.txt, sep=" ")
+size.txt <- file.path(output.path, "size.txt")
+write.table(size, file=size.txt, sep=" ")
+
+# Connect to SQL database via odbcConnect
 
-##Connect to SQL database via odbcConnect
-install.packages("RODBC")
 library(RODBC)
+
 channel<-odbcDriverConnect(connection=connectionString)
-##Read Kmeans_Result from SQL via OSR
-Kmeans.df<- sqlQuery(channel,paste("select * from dbo.Kmeans_Result"))
+
+# Read Kmeans_Result from SQL via OSR
+
+Kmeans.df <- sqlQuery(channel, paste("select * from dbo.Kmeans_Result"))
+
 head(Kmeans.df)
-plot(Kmeans.df[,2:4],col=Kmeans.df$X_rxCluster)
-title(main="RFM-based K-means on CDNOW Data",line=3)	
+plot(Kmeans.df[, 2:4], col=Kmeans.df$X_rxCluster)
+title(main="RFM-based K-means on CDNOW Data", line=3)	
 
-##Step 3: Prediction-logistic and decision tree
-#Create IsVIP variable
-RFMVIPData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFMVIP")
-rxDataStep(inData=RFMData,outFile=RFMVIPData,
-         transforms=list(IsVIP=ifelse(Toltal_Score>=441,1,0)	             
-			),
-        overwrite=TRUE,reportProgress=1
-)
-rxGetInfo(RFMVIPData,getVarInfo=T,numRows=3)
-rxGetInfo(RFMVIPRDData,getVarInfo=T,numRows=3)
-RFMVIPRDData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFMVIPRD")
-RFMVIPTrainTestData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFMVIPTrainTest")	
-rxDataStep(inData=RFMVIPRDData,outFile=RFMVIPTrainTestData,
-	       transforms=list(
-			urv=factor(ifelse(RD<=8,'TRAIN','TEST'))
-			),
+# Step 3: Prediction-logistic and decision tree
+
+# Create IsVIP variable
+
+RFMVIPData <- RxSqlServerData(connectionString=connectionString,
+                              table="RFMVIP")
+
+rxDataStep(inData=RFMData,
+           outFile=RFMVIPData,
+           transforms=list(IsVIP=ifelse(Toltal_Score >= 441, 1, 0)),
+           overwrite=TRUE,
+           reportProgress=1)
+
+rxGetInfo(RFMVIPData, getVarInfo=T, numRows=3)
+rxGetInfo(RFMVIPRDData, getVarInfo=T, numRows=3)
+
+RFMVIPRDData <- RxSqlServerData(connectionString=connectionString,
+                                table="RFMVIPRD")
+
+RFMVIPTrainTestData <- RxSqlServerData(connectionString = connectionString,
+                                       table="RFMVIPTrainTest")	
+
+rxDataStep(inData=RFMVIPRDData,
+           outFile=RFMVIPTrainTestData,
+	         transforms=list(urv=factor(ifelse(RD <= 8,'TRAIN','TEST'))),
            overwrite=T)
-rxGetInfo(RFMVIPTrainTestData,T,numRows=3)
 
-##Split data into training/testing data set
-TrainData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFMVIPTrainTest.urv.TRAIN")	
-TestData <- RxSqlServerData(
-         connectionString = connectionString,
-         table = "RFMVIPTrainTest.urv.TEST")	
-rxSplit(RFMVIPTrainTestData,outFilesBase=RFMVIPTrainTestData,splitByFactor='urv',overwrite=T,reportProgress=1)
+rxGetInfo(RFMVIPTrainTestData, T, numRows=3)
 
-##Build our Logistic Regression Model with IsVIP as response
-r1<- rxLogit(IsVIP ~R+F+M,
-data =RFMVIPData,
-variableSelection = rxStepControl(method="stepwise",
-scope = ~ R+F+M))
-summary(r1)
+## Split data into training/testing data set
 
-##r1:stepwise selection shows that Monetary,Recency are significant.
-##Build our Logistic REgression MOdel
-r2<- rxLogit(IsVIP ~R+F,
-data =RFMVIPData,covCoef=TRUE)
+TrainData <- RxSqlServerData(connectionString=connectionString,
+                             table = "RFMVIPTrainTest.urv.TRAIN")	
+
+TestData <- RxSqlServerData(connectionString=connectionString,
+                            table="RFMVIPTrainTest.urv.TEST")	
+
+rxSplit(RFMVIPTrainTestData, outFilesBase=RFMVIPTrainTestData,
+        splitByFactor='urv', overwrite=T, reportProgress=1)
+ 
+## Built our Logistic Regression Model with IsVIP as response
+
+r1<- rxLogit(IsVIP~R+F+M,
+             data =RFMVIPData,
+             variableSelection = rxStepControl(method="stepwise",
+             scope = ~ R+F+M))
+             summary(r1)
+
+## r1:stepwise selection shows that Monetary, Recency are significant.
+## Build our Logistic Regression Model
+             
+r2 <- rxLogit(IsVIP~R+F,
+              data=RFMVIPData, covCoef=TRUE)
 summary(r2)
 
-##Predict our Logistic Model on our test Dataset
-LogisticPred.xdf<-file.path(output.path,"LogisticPred.xdf")
-p2<-rxPredict(r2,data=test.xdf,outData=LogisticPred.xdf,writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=TRUE,computeStdErr = TRUE,
-interval = "confidence",predVarNames='LogitPredict',overwrite=TRUE)
-rxGetInfo(LogisticPred.xdf,getVarInfo=T,numRows=10)
-LogisticPredInfoTop10<-rxGetInfo(LogisticPred.xdf,getVarInfo=T,numRows=10)
-LogisticPredInfoTop10.txt<-file.path(output.path,"LogisticPredInfoTop10.txt")
-write.table(LogisticPredInfoTop10$data,file=LogisticPredInfoTop10.txt,sep=" ")
+## Predict our Logistic Model on our test Dataset
+
+LogisticPred.xdf <- file.path(output.path,"LogisticPred.xdf")
+p2 <- rxPredict(r2, data=test.xdf, outData=LogisticPred.xdf, 
+                writeModelVars=TRUE, extraVarsToWrite="ID", 
+                computeResiduals=TRUE, computeStdErr=TRUE,
+                interval="confidence", predVarNames='LogitPredict',
+                overwrite=TRUE)
+
+rxGetInfo(LogisticPred.xdf, getVarInfo=T, numRows=10)
+LogisticPredInfoTop10 <- rxGetInfo(LogisticPred.xdf, getVarInfo=T, numRows=10)
+LogisticPredInfoTop10.txt <- file.path(output.path, "LogisticPredInfoTop10.txt")
+write.table(LogisticPredInfoTop10$data, file=LogisticPredInfoTop10.txt, sep=" ")
+
+## Draw a ROC curve
 
-##Draw a ROC curve
 rxRocCurve(actualVarName='IsVIP',predVarNames='LogitPredict',data=LogisticPred.xdf)
 
-##Build a Decision Tree with Cluster as response
-d1 <- rxDTree(Cluster~R+F+M,data=TrainData,blocksPerRead=5)
-d1 <- rxDTree(Cluster~R_Score+F_Score+M_Score,data=TrainData,blocksPerRead=5)
+## Build a Decision Tree with Cluster as response
+
+d1 <- rxDTree(Cluster~R+F+M, data=TrainData, blocksPerRead=5)
+d1 <- rxDTree(Cluster~R_Score+F_Score+M_Score, data=TrainData, blocksPerRead=5)
 d1
 d1Cp<- rxDTreeBestCp(d1)
 d1 <- prune.rxDTree(d1, cp=d1Cp)
-d2 <- rxDTree(Cluster~R+F+M,data=TrainData, pruneCp="auto")
-d2 <- rxDTree(Cluster~R_Score+F_Score+M_Score,data=TrainData, pruneCp="auto")
+d2 <- rxDTree(Cluster~R+F+M, data=TrainData, pruneCp="auto")
+d2 <- rxDTree(Cluster~R_Score+F_Score+M_Score, data=TrainData, pruneCp="auto")
 d2
 
-#View Decision Tree
-#View 1
+# View Decision Tree
+
+# View 1
+
 library(RevoTreeView)
+
 plot(createTreeView(d1))
 plot(createTreeView(d2))
 
-#View 2
+# View 2
+
 library(rpart)
+
 plot(rxAddInheritance(d1))
 text(rxAddInheritance(d1))
-title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
+title(main="RFM-based Decision Tree on CDNOW Data",line=3)
+
 plot(rxAddInheritance(d2))
 text(rxAddInheritance(d2))
 title(main="RFM-based Decision Tree on CDNOW Data",line=3)	
 
-#Prediction
-DTreePred.xdf<-file.path(output.path,"DTreePred.xdf")
-rxPredict(d1, data=test.xdf,outData=DTreePred.xdf,writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=T,overwrite=TRUE)
-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=3)
-DTreePredInfoTop10<-rxGetInfo(DTreePred.xdf,getVarInfo=T,numRows=10)
-DTreePredInfoTop10.txt<-file.path(output.path,"DTreePredInfoTop10.txt")
-write.table(DTreePredInfoTop10$data,file=DTreePredInfoTop10.txt,sep=" ")
+# Prediction
 
-DTreePred<-rxXdfToDataFrame(file=DTreePred.xdf)
-sqlSave(channel,DTreePred,rownames=FALSE,append=FALSE,varTypes=list(numeric="float",integer="int",Date="date"))
+DTreePred.xdf<-file.path(output.path,"DTreePred.xdf")
+rxPredict(d1, data=test.xdf, outData=DTreePred.xdf, 
+          writeModelVars=TRUE, extraVarsToWrite="ID",
+          computeResiduals=T, overwrite=TRUE)
+
+rxGetInfo(DTreePred.xdf, getVarInfo=T, numRows=3)
+
+DTreePredInfoTop10 <- rxGetInfo(DTreePred.xdf, getVarInfo=T, numRows=10)
+DTreePredInfoTop10.txt <- file.path(output.path, "DTreePredInfoTop10.txt")
+write.table(DTreePredInfoTop10$data, file=DTreePredInfoTop10.txt, sep=" ")
+
+DTreePred <- rxXdfToDataFrame(file=DTreePred.xdf)
+
+sqlSave(channel, DTreePred, rownames=FALSE, append=FALSE, 
+        varTypes=list(numeric="float",
+                      integer="int",
+                      Date="date"))
 odbcClose(channel)
 
 
diff --git a/samples/features/r-services/Retail Precision Marketing/README.md b/samples/features/r-services/Retail Precision Marketing/README.md
index e0554aaf..dfe7772e 100644
--- a/samples/features/r-services/Retail Precision Marketing/README.md	
+++ b/samples/features/r-services/Retail Precision Marketing/README.md	
@@ -43,6 +43,10 @@ This sample consists of the following directory structure.
 
 To jumpstart, run the T-SQL files (crm_demo.sql)
 
+**Note**
+
+This is a demo built on SQL 2016 RC1 in Dec 2015. To try out it, please modify it to fit the new version of SQL Server R Services. 
+
 
 
 
diff --git a/samples/features/r-services/Retail Precision Marketing/SQL/crm_demo.sql b/samples/features/r-services/Retail Precision Marketing/SQL/crm_demo.sql
index e5db186b..fa49eeed 100644
--- a/samples/features/r-services/Retail Precision Marketing/SQL/crm_demo.sql	
+++ b/samples/features/r-services/Retail Precision Marketing/SQL/crm_demo.sql	
@@ -1,4 +1,5 @@
 --use the database RREDemoSql
+
 use sqlr;
 go
 
@@ -6,6 +7,7 @@ drop procedure if exists get_CDNOW_RFM
 go
 
 --create stored procedure to get RFM 
+
 create proc get_CDNOW_RFM (@start datetime = '1900-1-1', @end datetime = '3000-1-1', @now datetime = null) 
 as
 begin
@@ -27,12 +29,14 @@ end
 go
 
 --execute the stored procedure to obtain CDNOWRFM table
+
 exec dbo.get_CDNOW_RFM @start='1997-1-1',@end='1998-7-1',@now='1998-7-1'
 go
 
 drop procedure if exists BreakScoreRFM
 go
 
+--create stored procedure to break RFM score
 
 create proc BreakScoreRFM (@start datetime = '1900-1-1', @end datetime = '3000-1-1', @now datetime = null, 
       @r_cut varchar(254) = null, @f_cut varchar(254) = null, @m_cut varchar(254) = null) 
@@ -175,10 +179,13 @@ end
 
 go
 
+--execute the stored procedure to obtain RFM_Score table
+
 exec dbo.BreakScoreRFM @start ='1997-1-1', @end = '1998-7-1' ,@now = '1998-7-1', @r_cut ='142-433-486-513', @f_cut = '1-1-2-4', @m_cut ='14.37-20.25-29.37-44.29'
 go
 
 --combine RFM and RFM_Score
+
 drop table RFM_Result;
 select a.*, b.R_Score, b.F_Score, b.M_Score, b.Toltal_Score
 into RFM_Result
@@ -188,7 +195,8 @@ from
       a.[ID] = b.[ID];
 select top 10 * from RFM_Result;
 
---visualize RFM
+--create stored procedure to visualize RFM
+
 drop procedure if exists visualizeRFM;
 go
 create procedure visualizeRFM
@@ -235,6 +243,7 @@ grant execute on visualizeRFM to rdemo;
 go
 
 --clustering based on RFM
+
 drop table if exists Kmeans_Result;
 drop table if exists CDNOW_rx_models;
 go
@@ -251,6 +260,8 @@ create table Kmeans_Result (
 );
 go
 
+--create stored procedure to do clustering
+
 drop procedure if exists generate_CDNOW_rx_Kmeans;
 go
 create procedure generate_CDNOW_rx_Kmeans
@@ -261,13 +272,13 @@ begin
 	, @script = N'
 		require("RevoScaleR");
 		CDNOWKmeans <- rxKmeans(formula=~R+F+M+R_Score+F_Score+M_Score, 
-						data = RFM_Result, 
- 						#outFile =Kmeans_Result,
-						numClusters=8,
-						algorithm = "lloyd",
-						writeModelVars=TRUE,
-						overwrite=TRUE)
-		rxKmeans_model <- data.frame(payload = as.raw(serialize(CDNOWKmeans, connection=NULL)));
+						                data=RFM_Result, 
+ 						                #outFile=Kmeans_Result,
+						                numClusters=8,
+					                	algorithm="lloyd",
+						                writeModelVars=TRUE,
+					                	overwrite=TRUE)
+		rxKmeans_model <- data.frame(payload=as.raw(serialize(CDNOWKmeans, connection=NULL)));
 '
 	, @input_data_1 = N'select * from RFM_Result'
 	, @input_data_1_name = N'RFM_Result'
@@ -275,7 +286,8 @@ begin
 	with result sets ((model varbinary(max)));
 end;
 go
---how to write Kmeans_Result back to database?
+
+--how to write Kmeans_Result back to database?[To Be Modified]
 
 insert into CDNOW_rx_models (model)
 exec generate_CDNOW_rx_Kmeans;
@@ -283,7 +295,7 @@ update CDNOW_rx_models set model_name = 'rxKmeans' where model_name = 'default m
 select * from CDNOW_rx_models;
 go
 
---cannot write result table back to data base in rxKmeans. 
+--create stored procedure to build logistic regression model
 
 drop procedure if exists generate_CDNOW_rx_Logit;
 go
@@ -294,11 +306,11 @@ begin
 	  @language = N'R'
 	, @script = N'
 		require("RevoScaleR");
-		CDNOWLogit<- rxLogit(IsVIP~R+F+M,
-                     data =RFMVIPCluster,
-                     variableSelection = rxStepControl(method="stepwise",
-                     scope = ~ R+F+M))
-        summary(CDNOWLogit)
+		CDNOWLogit <- rxLogit(IsVIP~R+F+M,
+                          data=RFMVIPCluster,
+                          variableSelection=rxStepControl(method="stepwise",
+                          scope=~R+F+M))
+    summary(CDNOWLogit)
 		rxLogit_model <- data.frame(payload = as.raw(serialize(CDNOWLogit, connection=NULL)));
 '
 	, @input_data_1 = N'select * from RFMVIPCluster'
@@ -314,6 +326,8 @@ update CDNOW_rx_models set model_name = 'rxLogit' where model_name = 'default mo
 select * from CDNOW_rx_models;
 go
 
+--create stored procedure to build decision tree model
+
 drop procedure if exists generate_CDNOW_rx_Dtree;
 go
 create procedure generate_CDNOW_rx_Dtree
@@ -323,8 +337,8 @@ begin
 	  @language = N'R'
 	, @script = N'
 		require("RevoScaleR");
-		CDNOWDtree <- rxDTree(Cluster~R+F+M,data=RFMVIPCluster, pruneCp="auto")
-		rxDtree_model <- data.frame(payload = as.raw(serialize(CDNOWDtree, connection=NULL)));
+		CDNOWDtree <- rxDTree(Cluster~R+F+M, data=RFMVIPCluster, pruneCp="auto")
+		rxDtree_model <- data.frame(payload=as.raw(serialize(CDNOWDtree, connection=NULL)));
 '
 	, @input_data_1 = N'select * from RFMVIPCluster'
 	, @input_data_1_name = N'RFMVIPCluster'
@@ -339,6 +353,7 @@ update CDNOW_rx_models set model_name = 'rxDtree' where model_name = 'default mo
 select * from CDNOW_rx_models;
 go
 
+--create stored procedure to predict whether the customer is VIP or not
 
 drop procedure if exists predict_CDNOW_IsVIP;
 go
@@ -350,12 +365,12 @@ begin
 	exec sp_execute_external_script 
 					@language = N'R'
 				  , @script = N'
-require("RevoScaleR");
-CDNOWmodel<-unserialize(rx_model);
-CDNOWpred<-rxPredict(CDNOWmodel,data=RFMVIPCluster,writeModelVars = TRUE);
-OutputDataSet <- cbind(RFMVIPCluster[,1], CDNOWpred$IsVIP, round(CDNOWpred$IsVIP_Pred,2));
-colnames(OutputDataSet) <- c("ID", "IsVIP.Actual", "IsVIP.Expected");
-OutputDataSet<-as.data.frame(OutputDataSet);
+          require("RevoScaleR");
+          CDNOWmodel <- unserialize(rx_model);
+          CDNOWpred <- rxPredict(CDNOWmodel, data=RFMVIPCluster, writeModelVars = TRUE);
+          OutputDataSet <- cbind(RFMVIPCluster[,1], CDNOWpred$IsVIP, round(CDNOWpred$IsVIP_Pred,2));
+          colnames(OutputDataSet) <- c("ID", "IsVIP.Actual", "IsVIP.Expected");
+          OutputDataSet <- as.data.frame(OutputDataSet);
 '
 	, @input_data_1 = N'
 	select * from RFMVIPCluster'
@@ -367,9 +382,13 @@ OutputDataSet<-as.data.frame(OutputDataSet);
 end;
 go
 
+--execute the stored procedure to obtain the prediction on IsVIP
+
 exec predict_CDNOW_IsVIP 'rxLogit';
 go
 
+--create stored procedure to predict which cluster the customer belongs to
+
 drop procedure if exists predict_CDNOW_Cluster;
 go
 create procedure predict_CDNOW_Cluster (@model varchar(100))
@@ -380,15 +399,20 @@ begin
 	exec sp_execute_external_script 
 					@language = N'R'
 				  , @script = N'
-require("RevoScaleR");
-CDNOWmodel<-unserialize(rx_model);
-CDNOWpred<-rxPredict(CDNOWmodel,data=RFMVIPCluster, predVarNames=c("prob1","prob2","prob3","prob4","prob5","prob6","prob7","prob8"),writeModelVars = TRUE,extraVarsToWrite="ID",computeResiduals=T,overwrite=TRUE);
-OutputDataSet <- round(cbind(RFMVIPCluster[,1], RFMVIPCluster[,10], 
-                       CDNOWpred$prob1,CDNOWpred$prob2,CDNOWpred$prob3,CDNOWpred$prob4,
-					   CDNOWpred$prob5,CDNOWpred$prob6,CDNOWpred$prob7,CDNOWpred$prob8),2);
-colnames(OutputDataSet) <- c("ID", "Cluster.Actual", "Cluster1.Prob","Cluster2.Prob","Cluster3.Prob","Cluster4.Prob","Cluster5.Prob","Cluster6.Prob","Cluster7.Prob","Cluster8.Prob");
-OutputDataSet<-as.data.frame(OutputDataSet);
-'
+          require("RevoScaleR");
+          CDNOWmodel <- unserialize(rx_model);
+          CDNOWpred <- rxPredict(CDNOWmodel,
+                                 data=RFMVIPCluster, 
+                                 predVarNames=c("prob1", "prob2", "prob3", "prob4", "prob5", "prob6", "prob7", "prob8"),
+                                 writeModelVars=TRUE, extraVarsToWrite="ID",
+                                 computeResiduals=T, overwrite=TRUE);
+          OutputDataSet <- round(cbind(RFMVIPCluster[,1], RFMVIPCluster[,10], 
+                                CDNOWpred$prob1,CDNOWpred$prob2,CDNOWpred$prob3,CDNOWpred$prob4,
+					                      CDNOWpred$prob5,CDNOWpred$prob6,CDNOWpred$prob7,CDNOWpred$prob8),2);
+					                      
+          colnames(OutputDataSet) <- c("ID", "Cluster.Actual", "Cluster1.Prob","Cluster2.Prob","Cluster3.Prob","Cluster4.Prob","Cluster5.Prob","Cluster6.Prob","Cluster7.Prob","Cluster8.Prob");
+          OutputDataSet<-as.data.frame(OutputDataSet);
+  '
 	, @input_data_1 = N'
 	select * from RFMVIPCluster'
 	, @input_data_1_name = N'RFMVIPCluster'
@@ -399,5 +423,7 @@ OutputDataSet<-as.data.frame(OutputDataSet);
 end;
 go
 
+--execute the stored procedure to obtain the prediction on Cluster
+
 exec predict_CDNOW_Cluster 'rxDtree';
 go
\ No newline at end of file
diff --git a/samples/features/r-services/Retail Precision Marketing/SQL/crm_setup.sql b/samples/features/r-services/Retail Precision Marketing/SQL/crm_setup.sql
index 149e100e..4d1d8ded 100644
--- a/samples/features/r-services/Retail Precision Marketing/SQL/crm_setup.sql	
+++ b/samples/features/r-services/Retail Precision Marketing/SQL/crm_setup.sql	
@@ -2,20 +2,25 @@ use sqlr;
 go
 drop table if exists CDNOW;
 go
+
 -- create the fraud table to hold invoice data:
+
 create table CDNOW(
 	[ID] int not null,
 	[Date] date not null,
 	[Volume] int not null,
 	[Amount] float not null);
 go
--- Modify path to the data file: "po.txt"
+
+-- Modify path to the data file: "CDNOW_master.csv"
+
 bulk insert CDNOW
 from 'C:\sqlr\mydemos\CRM\CDNOW_master.csv'
 with(
 	fieldterminator = ',',
 	firstrow = 2);
 go
+
 --create clustered columnstore index cs_CDNOW on CDNOW;
 --go